A charging demand prediction method for individual electric vehicle users based on dual-layer multisource data clustering and a LightGBM

• A method for predicting individual EV user charging demand using multisource data, dual-layer clustering, and LightGBM is proposed. • A multisource dataset combining EV behavior, weather, and holiday data is used to improve prediction accuracy. • A dual-layer clustering model combining HDBSCAN for charging type classification and K-means for EV user classification is proposed, creating a feature space that characterizes individual charging behaviors. • LightGBM is used for charging demand prediction, with an analysis of how clustering and external factors affect accuracy. The charging behaviors of electric vehicle (EV) users exhibit high randomness and individual heterogeneity, with the key parameters such as the charging duration and charged energy levels displaying significant fluctuations. Compared with EV cluster-layer prediction, predicting the charging demands of individual users requires not only the analysis of more complex charging behaviors but also the establishment of a coupling model between exogenous variables (e.g., weather and holidays) and prediction accuracy, thereby imposing higher robustness requirements on prediction algorithms. An individual-user EV charging demand prediction method that integrates multisource data with a dual-layer clustering approach and a light gradient boosting machine (LightGBM) is proposed in this study to address these technical challenges. First, a multisource dataset that incorporates user charging behavior data and exogenous variables (meteorological factors and date types) is constructed. A dual-layer feature extraction mechanism consisting of data-layer clustering for charging type identification and user-layer clustering for user group classification is employed, thereby establishing a classification feature space that characterizes different charging types and user groups. A predictive model is subsequently developed using the LightGBM algorithm, which directly incorporates classification features as its inputs, effectively mitigating the information loss associated with the traditional categorical variable encoding process. Finally, employing EV users from a typical residential community in northern China as an empirical case, comparative experiments are performed to validate the proposed method, demonstrating its effectiveness at improving prediction accuracy.